How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes

TL;DR

This paper explores how large language models can generate educational questions aligned with Bloom's taxonomy, demonstrating that teachers prefer and can effectively use these questions without quality loss, enhancing quiz creation.

Contribution

It introduces a pedagogically informed question generation approach using large language models and evaluates its practical use by teachers in classroom settings.

Findings

Teachers prefer using automatically generated questions for quizzes.

Generated questions match the quality of handwritten questions.

Automated questions can improve quiz quality metrics.

Abstract

Question generation (QG) is a natural language processing task with an abundance of potential benefits and use cases in the educational domain. In order for this potential to be realized, QG systems must be designed and validated with pedagogical needs in mind. However, little research has assessed or designed QG approaches with the input from real teachers or students. This paper applies a large language model-based QG approach where questions are generated with learning goals derived from Bloom's taxonomy. The automatically generated questions are used in multiple experiments designed to assess how teachers use them in practice. The results demonstrate that teachers prefer to write quizzes with automatically generated questions, and that such quizzes have no loss in quality compared to handwritten versions. Further, several metrics indicate that automatically generated questions can even improve the quality of the quizzes created, showing the promise for large scale use of QG in the classroom setting.